基于改进混合高斯模型的人群密度估计方法

人群密度估计对于公共安全管理至关重要。针对视频监控系统下的人群密度估计问题，提出了一种基于改进混合高斯模型和像素统计的人群密度估计方法。通过计算图像的均值和偏差均值，提取高斯模型特征，在恒定的模型更新速率指导下，重建混合高斯背景图，从而获取人群二值图，最后，利用像素统计的方法实现人群密度快速估计。实验结果表明，较传统方法，该方法可以更准确有效地估计人群密度。

Crowd Density Estimation Based on Improved Gaussian Mixture Model

Crowd density estimation is very important to public security.To the question of crowd density estimation in video surveillance system,a crowd density estimation technique based on improved Gaussian mixture model and pixel statistics was proposed.The feature of Gaussian model was abstracted by calculating the mean and the mean of deviation in the image.Under the direction of the constant updating rate of the model,the crowd binary images were achieved by background reconstruction using Gaussian mixture model.Finally pixel statistics was used to realize the fast estimation of crowd density.Experimental results show that the new algorithm of crowd density estimation is more accurate and efficient than previous one.